
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
pd.set_option('display.max_rows', None)
import plotly.offline as py
import plotly.graph_objects as go
import plotly.express as px
from IPython.display import HTML,display,display_html
import random


def hide_toggle(for_next=False):
    this_cell = """$('div.cell.code_cell.rendered.selected')"""
    next_cell = this_cell + '.next()'

    toggle_text = 'Toggle show/hide'  # text shown on toggle link
    target_cell = this_cell  # target cell to control with toggle
    js_hide_current = ''  # bit of JS to permanently hide code in current cell (only when toggling next cell)

    if for_next:
        target_cell = next_cell
        toggle_text += ' next cell'
        js_hide_current = this_cell + '.find("div.input").hide();'

    js_f_name = 'code_toggle_{}'.format(str(random.randint(1,2**64)))

    html = """
        <script>
            function {f_name}() {{
                {cell_selector}.find('div.input').toggle();
            }}

            {js_hide_current}
        </script>

        <a href="javascript:{f_name}()">{toggle_text}</a>
    """.format(
        f_name=js_f_name,
        cell_selector=target_cell,
        js_hide_current=js_hide_current,
        toggle_text=toggle_text
    )

    return HTML(html)

hide_toggle()


df = pd.read_csv(r'C:\Users\Chongkyung\Documents\github\fifa_analysis\FIFA-21 Complete.csv',sep=';', index_col='player_id')

# in the FIFA21 player database, the team names had a space at the end: truncated.
df['team'] = df['team'].str.rstrip()

hide_toggle()


team_stat = df.groupby(['team'])['overall'].mean().sort_values(ascending=False)
team_stat.head(15).to_frame().style.background_gradient(cmap="magma")


best_itw=df.loc[df['overall'] > 84 ].sort_values(by = ['overall'],ascending=False)
best_itw['team'].value_counts().head(15).to_frame().style.background_gradient(cmap="twilight")


# Average Position Analysis
# Data pulled from Wikipedia
# (not many sites and data sources keep track of teams' mid-season league positions)
links = ["https://en.wikipedia.org/wiki/2009%E2%80%9310_Manchester_United_F.C._season","https://en.wikipedia.org/wiki/2010%E2%80%9311_Manchester_United_F.C._season","https://en.wikipedia.org/wiki/2011%E2%80%9312_Manchester_United_F.C._season","https://en.wikipedia.org/wiki/2012%E2%80%9313_Manchester_United_F.C._season","https://en.wikipedia.org/wiki/2013%E2%80%9314_Manchester_United_F.C._season","https://en.wikipedia.org/wiki/2014%E2%80%9315_Manchester_United_F.C._season","https://en.wikipedia.org/wiki/2015%E2%80%9316_Manchester_United_F.C._season","https://en.wikipedia.org/wiki/2016%E2%80%9317_Manchester_United_F.C._season","https://en.wikipedia.org/wiki/2017%E2%80%9318_Manchester_United_F.C._season","https://en.wikipedia.org/wiki/2018%E2%80%9319_Manchester_United_F.C._season","https://en.wikipedia.org/wiki/2019%E2%80%9320_Manchester_United_F.C._season"]
league_pos = []

def league_pos_extractor(link,league):
    manu_df = pd.read_html(link)
    if len(manu_df[4]) ==38:
        positions = manu_df[4].iloc[:,-1].values.tolist()
    elif link == "https://en.wikipedia.org/wiki/2018%E2%80%9319_Manchester_United_F.C._season":
        positions = manu_df[3].iloc[:,-2].values.tolist()
    else:
        positions = manu_df[3].iloc[:,-1].values.tolist()
    league.append(positions)

for link in links:
    league_pos_extractor(link,league_pos)
agg_lg_pos = pd.DataFrame(league_pos)

df2 = agg_lg_pos.T
df2.columns = ["09/10","10/11","11/12","12/13","13/14","14/15","15/16","16/17","17/18","18/19","19/20"]

for col in df2:
    if col == "15/16":
        df2[col] = df2[col].str[0]
    else:
        df2[col] = df2[col].str[:-2]
    df2[col] = pd.to_numeric(df2[col])

df2.index = range(1,39)

avg_pos = []

for col in df2:
    avg_pos.append((np.mean(df2[col])))

hide_toggle()


pd.options.plotting.backend = "plotly"

fig = df2.plot(labels = dict(index="Gameweek",value="League Position",variable="season",line_shape="hv"))
fig['layout']['yaxis']['autorange'] = "reversed"

fig.show(include_plotlyjs=False,output_type='div')


years = df2.columns

# fig2 = go.Figure(data=[go.Table(header=dict(values=['Season','Average Position', 'Final Position']),
#                  cells=dict(values=[df2.columns,avg_pos, [2,1,2,1,7,4,5,6,2,6,3]]))
#                      ])

fig2 = go.Figure()
fig2.add_trace(go.Scatter(x=years, y=avg_pos,
                    mode='lines+markers',
                    name='Avg Position'))
fig2.add_trace(go.Scatter(x=years, y= [2,1,2,1,7,4,5,6,2,6,3],
                    mode='lines+markers',
                    name='Result Position'))
fig2['layout']['yaxis']['autorange'] = "reversed"

fig2.show()


pl_champs = ['Liverpool','Chelsea','Manchester City','Leicester City', 'Manchester United']


ratings = [[],[],[],[],[]]

df15 = pd.read_csv(r'C:\Users\Chongkyung\Documents\GitHub\fifa_analysis\fifa21\fifa15_20\players_15.csv')
df16 = pd.read_csv(r'C:\Users\Chongkyung\Documents\GitHub\fifa_analysis\fifa21\fifa15_20\players_16.csv')
df17 = pd.read_csv(r'C:\Users\Chongkyung\Documents\GitHub\fifa_analysis\fifa21\fifa15_20\players_17.csv')
df18 = pd.read_csv(r'C:\Users\Chongkyung\Documents\GitHub\fifa_analysis\fifa21\fifa15_20\players_18.csv')
df19 = pd.read_csv(r'C:\Users\Chongkyung\Documents\GitHub\fifa_analysis\fifa21\fifa15_20\players_19.csv')
df20 = pd.read_csv(r'C:\Users\Chongkyung\Documents\GitHub\fifa_analysis\fifa21\fifa15_20\players_20.csv')

def champ_mean(plchamps,df,lists):
    for champ,i in zip(plchamps,range(5)):
        lists[i].append(df.loc[df['club'] == champ ]['overall'].mean())

dfs = [df15,df16,df17,df18,df19,df20]

for df in dfs:
    champ_mean(pl_champs,df,ratings)

hide_toggle()


fig3 = go.Figure()

x = years[5:]

for i in range(5):
    fig3.add_trace(go.Scatter(x=x, y=ratings[i], name=pl_champs[i],
                    line_shape='linear'))
fig3.update_traces(hoverinfo='text+name', mode='lines+markers')
fig3.update_layout(legend=dict(y=0.5, traceorder='reversed', font_size=16))


def display_side_by_side(*args):
    html_str=''
    for df in args:
        html_str+=df.to_html()
    display_html(html_str.replace('table','table style="display:inline"'),raw=True)

hide_toggle()


mu_16_17=df17.loc[df17['club']=="Manchester United"].sort_values(by = ['overall'],ascending=False)
mu_16_17 = mu_16_17[['short_name','overall']].style.background_gradient(cmap="Reds").set_table_attributes("style='display:inline'").set_caption('16/17 Man Utd (6th) ')


mc_16_17=df17.loc[df17['club']=="Manchester City"].sort_values(by = ['overall'],ascending=False)
mc_16_17 = mc_16_17[['short_name','overall']].style.background_gradient(cmap="cool").set_table_attributes("style='display:inline'").set_caption('Man City (3rd)')

cfc_16_17=df17.loc[df17['club']=="Chelsea"].sort_values(by = ['overall'],ascending=False)
cfc_16_17 = cfc_16_17[['short_name','overall']].style.background_gradient(cmap="Blues").set_table_attributes("style='display:inline'").set_caption('Chelsea (1st)')

spurs_16_17=df17.loc[df17['club']=="Tottenham Hotspur"].sort_values(by = ['overall'],ascending=False)
spurs_16_17 = spurs_16_17[['short_name','overall']].style.background_gradient(cmap="gray").set_table_attributes("style='display:inline'").set_caption('Spurs (2nd)')

hide_toggle()


display_html(mu_16_17._repr_html_()+cfc_16_17._repr_html_()+spurs_16_17._repr_html_()+mc_16_17._repr_html_(),raw=True)

	overall
team
Juventus	83.043478
FC Bayern München	81.565217
Napoli	79.730769
Real Madrid	79.606061
Paris Saint-Germain	78.866667
Inter	78.576923
FC Barcelona	78.090909
Bayer 04 Leverkusen	77.916667
Manchester City	76.875000
SL Benfica	76.600000
Lazio	76.500000
Liverpool	76.454545
Atlético Madrid	76.424242
Milan	76.040000
Manchester United	76.030303

	team
FC Barcelona	12
Real Madrid	11
Manchester City	11
Liverpool	10
Paris Saint-Germain	9
FC Bayern München	8
Juventus	8
Inter	6
Tottenham Hotspur	5
Borussia Dortmund	5
Atlético Madrid	4
Manchester United	3
Lazio	3
Napoli	3
Arsenal	2

	short_name	overall
5	De Gea	90
6	Z. Ibrahimović	90
16	P. Pogba	88
81	H. Mkhitaryan	85
105	C. Smalling	84
111	W. Rooney	84
121	Juan Mata	84
153	B. Schweinsteiger	83
190	A. Martial	82
191	E. Bailly	82
215	M. Schneiderlin	82
226	Ander Herrera	82
270	L. Shaw	81
313	D. Blind	81
337	M. Carrick	81
481	A. Valencia	80
430	M. Darmian	80
388	M. Depay	80
569	P. Jones	79
641	A. Young	79
667	S. Romero	79
794	M. Rojo	78
892	M. Fellaini	78
972	J. Lingard	77
1236	M. Rashford	76
3900	T. Fosu-Mensah	71
6386	S. Johnstone	68
7313	A. Tuanzebe	67
10340	J. Riley	64
11278	R. Poole	63
14587	S. Goss	58
15124	S. El Fitouri	57

	short_name	overall
10	T. Courtois	89
20	E. Hazard	88
53	Cesc Fàbregas	86
66	Diego Costa	85
78	Willian	85
107	J. Terry	84
125	David Luiz	84
126	Azpilicueta	84
127	N. Matić	84
136	Oscar	83
166	G. Cahill	83
169	A. Begović	83
184	Pedro	83
281	M. Batshuayi	81
292	N. Kanté	81
497	B. Ivanović	80
383	K. Zouma	80
864	J. Mikel	78
964	M. van Ginkel	77
1013	V. Moses	77
1021	Marcos Alonso	77
1507	Eduardo	76
4766	T. Kane	70
5456	D. Solanke	69
6358	N. Chalobah	68
6344	R. Loftus-Cheek	68
11300	O. Aina	63
12802	F. Tomori	61
14058	J. Dasilva	59
14536	K. Scott	58
14997	M. Mount	57
15087	B. Collins	57
15764	T. Chalobah	55

	short_name	overall
27	H. Lloris	88
67	T. Alderweireld	85
85	H. Kane	84
87	C. Eriksen	84
170	J. Vertonghen	83
247	M. Dembélé	82
284	E. Lamela	81
296	K. Walker	81
382	D. Alli	80
428	D. Rose	80
459	M. Vorm	80
505	M. Sissoko	80
565	K. Wimmer	79
761	B. Davies	78
756	H. Son	78
751	V. Wanyama	78
742	G. Nkoudou	78
731	V. Janssen	78
716	E. Dier	78
1014	K. Trippier	77
2656	Pau López	73
3334	T. Carroll	72
4619	J. Onomah	70
10413	H. Winks	64
12117	C. Carter-Vickers	62
13471	S. Harrison	60
14519	M. Edwards	58
14543	K. Walker-Peters	58
14646	L. Amos	58
14662	C. Ogilvie	58
15067	C. Maghoma	57
15441	A. Walkes	56
16584	H. Voss	52

Manchester United Analysis¶

Let's look at the raw FIFA stats first and see how Manchester United compares to other clubs in the world and especially their PL rivals.¶

Intro: Basic comparisons between top European teams¶

Manchester United ranks 15th in the world, by mean FIFA21 overall stats;¶

Man City and Liverpool are the only PL clubs with higher mean overall stats¶

It is commonly accepted by FIFA fans that world class players have at least a 85 overall rating. By this popular metric, Manchester United has only 3 world class players, less than Man City's 11, Liverpool's 10, and even Tottenham's 5.¶

A Look inside Average League Positions for the past decade¶

We can see that since the 09/10 season, the team usually finishes at the position that it stays near for the entire season. The over-performance of 19/20 season, from an average to 6.2 to 3, can be attributed to Bruno Fernandes¶

As of today, 1/15/2021, Manchester United are 17 games in and has averaged about 10th place.¶

It is not surprising nor offensive when rival fans doubt Manchester United's long-term success.¶

If Manchester United wins ALL of the remaining 21 games¶

1. the team will have averaged about 5th place and¶

2. also win the league (as it stands, no team can overthrow United from 1st place)¶

At least for the past decade, no Manchester United side has had that big of a (mid-season) turnaround, bar the 19/20 season, so it remains questionable that Manchester United can mount as PL champions, although it seems like United may comfrotably title challengers and a top 4 side.¶

A look inside FIFA ratings (compared to top flight PL teams)¶

In recent history, ever since Manchester United last won the PL, four different teams have been crowned champions: Liverpool, Chelsea, Man City, and Leicester City¶

How did we compare to these teams? Let's see.¶

In the 16/17 season, Manchester United had by far the highest rated team in the PL and yet they finished 6th in the league. Let's look at how the squad actually compared to the top 3 teams that season.¶

	short_name	overall
12	S. Agüero	89
18	K. De Bruyne	88
38	David Silva	87
49	V. Kompany	86
65	I. Gündoğan	85
76	C. Bravo	85
80	N. Otamendi	85
108	Y. Touré	84
188	Nolito	83
196	R. Sterling	82
237	P. Zabaleta	82
248	B. Sagna	82
345	Fernandinho	81
467	Jesús Navas	80
471	G. Clichy	80
689	A. Kolarov	79
687	Fernando	79
527	L. Sané	79
710	J. Stones	78
782	F. Delph	78
836	W. Caballero	78
2078	K. Iheanacho	74
8365	T. Ambrose	66
9339	Pablo Maffeo	65
9352	Angeliño	65
9367	A. Gunn	65
10320	Brahim Díaz	64
11269	Aleix García	63
12784	C. Humphreys-Grant	61
12874	I. Lawlor	61
13445	W. Patching	60
13508	A. Nemane	60
14076	T. Adarabioyo	59